ABSTRACT
COVID-19 is a rapidly spreading illness around the globe, yet healthcare resources are limited. Timely screening of people who may have had COVID-19 is critical in reducing the virus's spread considering the lack of an effective treatment or medication. COVID-19 patients should be diagnosed as well as isolated as early as possible to avoid the infection from spreading and levelling the pandemic arc. To detect COVID-19, chest ultrasound tomography seems to be an option to the RT-PCR assay. The Ultrasound of the lung is a very precise, quick, relatively reliable surgical assay that can be used in conjunction with the RT PCR (Reverse Transcription Polymerase Chain Reaction) assay. Differential diagnosis is difficult due to large differences in structure, shape, and position of illnesses. The efficiency of conventional neural learning-based Computed tomography scans feature extraction is limited by discontinuous ground-glass and acquisitions, as well as clinical alterations. Deep learning-based techniques, primarily Convolutional Neural Networks (CNN), had successfully proved remarkable therapeutic outcomes. Moreover, CNNs are unable to capture complex features amongst images examples, necessitating the use of huge databases. In this paper semantic segmentation method is used. The semantic segmentation architecture U-Net is applied on COVID-19 CT images as well as another method is suggested based on prior semantic segmentation. The accuracy of U-Net is 87% and by using pre-trained U-Net with convolution layers gives accuracy of 89.07%. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
COVID-19 is one of the greatest pandemics that threaten individuals, especially the elders. It was first reported in Wuhan, China in 2019. It was discovered recently that COVID-19 disease can be detected using three main protocols. The first protocol is based on Polymerase Chain reaction (PCR), while the second protocol is based on lung chest (ultrasound, X-ray, and CT-Scan), and the final protocol is based on the ECG image reports. This review aims to present a survey on the methodologies and algorithms applied for the detection of COVID disease using electrocardiogram (ECG). In this study, various papers were presented for determining how the COVID can be diagnosed using ECG image reports relying on symptoms and changes in the ECG peaks and intervals. In addition to this, other studies are presented on techniques applied to the ECG reports for the detection of COVID. Also, the main limitations and future works are illustrated. It can be concluded that COVID can be detected with high accuracy using ECG reports and it is even more efficient than other protocols. Finally, based on the performance of the studies it can be shown that the ECG image report is close to an acceptable level in the detection of COVID disease. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
COVID-19 is one of the greatest pandemics that threaten individuals, especially the elders. It was first reported in Wuhan, China in 2019. It was discovered recently that COVID-19 disease can be detected using three main protocols. The first protocol is based on Polymerase Chain reaction (PCR), while the second protocol is based on lung chest (ultrasound, X-ray, and CT-Scan), and the final protocol is based on the ECG image reports. This review aims to present a survey on the methodologies and algorithms applied for the detection of COVID disease using electrocardiogram (ECG). In this study, various papers were presented for determining how the COVID can be diagnosed using ECG image reports relying on symptoms and changes in the ECG peaks and intervals. In addition to this, other studies are presented on techniques applied to the ECG reports for the detection of COVID. Also, the main limitations and future works are illustrated. It can be concluded that COVID can be detected with high accuracy using ECG reports and it is even more efficient than other protocols. Finally, based on the performance of the studies it can be shown that the ECG image report is close to an acceptable level in the detection of COVID disease. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has spread worldwide causing a global pandemic. In this context, lung ultrasound (LUS) has played an important role due to its high diagnostic sensitivity, low costs, simplicity of execution and radiation safeness. Despite computed tomography (CT) being the imaging gold standard, lung ultrasound point of care exam is essential in every situation where CT is not readily available nor applicable. The aim of our review is to highlight the considerable versatility of LUS in diagnosis, framing the therapeutic route and follow-up for SARS-CoV-2 interstitial syndrome.
ABSTRACT
Background: The COVID-19 pandemic has caused significant disruption to healthcare worldwide. In this study, we aim to quantify its impact of chest related radiological procedures over the different waves of local infection in Hong Kong across the territory's public hospitals. Methods: This was an observational study enrolling patients between January 2017 and December 2020. Consecutive population-based chest radiographs, CT, US, and interventional radiology (IR) procedures were obtained public hospitals across Hong Kong. Results: A significant reduction of 10.0% (p < 0.001) in the total number of chest radiographs was observed. Non-significant reduction of 2.5% (p = 0.0989), 39.1% (p = 0.2135), and 1.9% (p = 0.8446) was observed for Chest CT, Chest US, and Chest IR procedures, respectively, in 2020 compared to the projected values. Conclusion: Although, it was anticipated that there would be a significant impact to health services caused by the pandemic, for chest-related investigations in Hong Kong, the impact was not as severe. Quantitative analysis could help with future planning and public health decision making.
ABSTRACT
Although a plethora of research articles on AI methods on COVID-19 medical imaging are published, their clinical value remains unclear. We conducted the largest systematic review of the literature addressing the utility of AI in imaging for COVID-19 patient care. By keyword searches on PubMed and preprint servers throughout 2020, we identified 463 manuscripts and performed a systematic meta-analysis to assess their technical merit and clinical relevance. Our analysis evidences a significant disparity between clinical and AI communities, in the focus on both imaging modalities (AI experts neglected CT and ultrasound, favoring X-ray) and performed tasks (71.9% of AI papers centered on diagnosis). The vast majority of manuscripts were found to be deficient regarding potential use in clinical practice, but 2.7% (n = 12) publications were assigned a high maturity level and are summarized in greater detail. We provide an itemized discussion of the challenges in developing clinically relevant AI solutions with recommendations and remedies.
ABSTRACT
PURPOSE: The COVID-19 pandemic poses new challenges for the medical community due to its large number of patients presenting with varying symptoms. Chest ultrasound (ChUS) may be particularly useful in the early clinical management in suspected COVID-19 patients due to its broad availability and rapid application. We aimed to investigate patterns of ChUS in COVID-19 patients and compare the findings with results from chest X-ray (CRX). MATERIALS AND METHODS: 24 patients (18 symptomatic, 6 asymptomatic) with confirmed SARS-CoV-2 by polymerase chain reaction underwent bedside ChUS in addition to CRX following admission. Subsequently, the results of ChUS and CRX were compared. RESULTS: 94% (n=17/18) of patients with respiratory symptoms demonstrated lung abnormalities on ChUS. ChUS was especially useful to detect interstitial syndrome compared to CXR in COVID-19 patients (17/18 vs. 11/18; p<0.02). Of note, ChUS also detected lung consolidations very effectively (14/18 for ChUS vs. 7/18 cases for CXR; p<0.02). Besides pathological B-lines and subpleural consolidations, pleural line abnormality (89%; n=16/18) was the third most common feature in patients with respiratory manifestations of COVID-19 detected by ChUS. CONCLUSION: Our findings support the high value of ChUS in the management of COVID-19 patients.
ABSTRACT
BACKGROUND: Lung ultrasound (LUS) is an accurate, safe, and cheap tool assisting in the diagnosis of several acute respiratory diseases. The diagnostic value of LUS in the workup of coronavirus disease-19 (COVID-19) in the hospital setting is still uncertain. OBJECTIVES: The aim of this observational study was to explore correlations of the LUS appearance of COVID-19-related pneumonia with CT findings. METHODS: Twenty-six patients (14 males, age 64 ± 16 years) urgently hospitalized for COVID-19 pneumonia, who underwent chest CT and bedside LUS on the day of admission, were enrolled in this observational study. CT images were reviewed by expert chest radiologists, who calculated a visual CT score based on extension and distribution of ground-glass opacities and consolidations. LUS was performed by clinicians with certified competency in thoracic ultrasonography, blind to CT findings, following a systematic approach recommended by ultrasound guidelines. LUS score was calculated according to presence, distribution, and severity of abnormalities. RESULTS: All participants had CT findings suggestive of bilateral COVID-19 pneumonia, with an average visual scoring of 43 ± 24%. LUS identified 4 different possible -abnormalities, with bilateral distribution (average LUS score 15 ± 5): focal areas of nonconfluent B lines, diffuse confluent B lines, small subpleural microconsolidations with pleural line irregularities, and large parenchymal consolidations with air bronchograms. LUS score was significantly correlated with CT visual scoring (r = 0.65, p < 0.001) and oxygen saturation in room air (r = -0.66, p < 0.001). CONCLUSION: When integrated with clinical data, LUS could represent a valid diagnostic aid in patients with suspect COVID-19 pneumonia, which reflects CT findings.